Data Engineer

About Acuative

Acuative is a globally recognized technology services provider specializing in delivering cutting-edge network, IT, and digital transformation solutions to enterprise clients, service providers, and public-sector organizations. With a robust regional presence and a proven track record of executing large-scale, mission-critical projects, Acuative combines deep technical expertise with a service-first approach. As part of our ongoing expansion in the data and analytics domain, we are seeking dynamic professionals to contribute to a strategic data platform and business intelligence initiative.

Role Overview

Acuative is looking for a proficient Data Engineer to design, develop, and maintain the foundational data pipelines that power an enterprise-grade Lakehouse architecture and business intelligence framework. In this pivotal role, you will be responsible for constructing automated data ingestion pipelines, implementing efficient ETL/ELT frameworks, optimizing data transformations, and ensuring the seamless flow of high-quality, reliable data across all layers of the platform. Your contributions will be instrumental in enabling data-driven decision-making and supporting the organization’s strategic objectives.

Key Responsibilities

• Data Ingestion: Develop and maintain automated data ingestion pipelines that extract, transform, and load data from diverse source systems, ensuring timely and accurate data availability.
• ETL/ELT Frameworks: Design, build, and extend reusable ETL/ELT frameworks that align with the platform’s architectural standards, promoting scalability, maintainability, and performance.
• Data Transformation: Develop and optimize data transformation scripts and jobs to enhance processing efficiency, reduce operational costs, and improve overall system performance.
• Data Quality & Monitoring: Implement robust data quality checks, validation rules, and real-time monitoring mechanisms to ensure the integrity, consistency, and reliability of data across all pipelines.
• Pipeline Orchestration: Utilize advanced orchestration tools such as Azure Data Factory or Apache Airflow to schedule, manage, and monitor data workflows, ensuring seamless execution and minimal downtime.
• Collaboration & Delivery: Work closely with architecture and business intelligence teams to deliver curated, analytics-ready datasets that support reporting, analytics, and strategic initiatives.
• Documentation & Governance: Maintain comprehensive documentation of pipelines, frameworks, and operational procedures to facilitate knowledge sharing, troubleshooting, and compliance with organizational standards.

Required Qualifications

• Experience: Minimum of 5 years of hands-on experience in data engineering, with a proven ability to design and implement scalable data solutions.
• Technical Proficiency: Strong expertise in Python, SQL, and Apache Spark, with the ability to develop high-performance data processing solutions.
• Cloud & Modern Platforms: Hands-on experience with cloud-based data tools and modern data platform ecosystems, including but not limited to cloud data warehouses, data lakes, and big data technologies.
• Pipeline Orchestration: Practical experience with pipeline orchestration tools such as Azure Data Factory or Apache Airflow, ensuring efficient workflow management and execution.
• Data Quality & ETL/ELT: Solid understanding of data quality management principles and best practices in ETL/ELT processes, with a commitment to delivering accurate and reliable data outputs.

Preferred Qualifications

• Lakehouse Platforms: Experience with Lakehouse platforms such as Databricks, Synapse, or Snowflake, enabling seamless integration and management of structured and unstructured data.
• CI/CD & DevOps: Familiarity with Continuous Integration/Continuous Deployment (CI/CD) and DevOps practices for data pipelines, promoting automation, version control, and rapid deployment.
• Certifications: Relevant cloud or data engineering certifications that demonstrate your expertise and commitment to professional development in the field.